Progress Update:
This week, I focused on implementing smoothing algorithms and moving averages to process the incoming data. These techniques help in reducing noise and making the data more suitable for analysis. I also worked on a thresholding algorithm that uses the pitch of the device as a means of detecting footstrikes. By analyzing the changes in pitch, we can more accurately determine when a footstrike occurs.
Challenges Faced:
- Algorithm Optimization: Ensuring the smoothing and moving average algorithms do not distort the essential features of the data needed for accurate footstrike detection.
- Threshold Calibration: Determining the optimal threshold values for pitch changes to reliably detect footstrikes without false positives.
Next Steps:
- Validate the smoothing and thresholding algorithms with a larger dataset.
- Integrate the footstrike detection feature into the main application.
- Collect feedback from initial tests to further refine the algorithms.